haku: @supervisor Virrantaus, Kirsi / yhteensä: 163
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| Tekijä: | Kallio, Marko |
| Työn nimi: | An exploration of water poverty in Lao People's Democratic Republic |
| Julkaisutyyppi: | Diplomityö |
| Julkaisuvuosi: | 2016 |
| Sivut: | (11) + 88 s. + liitt. 27 Kieli: eng |
| Koulu/Laitos/Osasto: | Insinööritieteiden korkeakoulu |
| Oppiaine: | Geoinformation Technology (IA3002) |
| Valvoja: | Virrantaus, Kirsi |
| Ohjaaja: | Virrantaus, Kirsi |
| Elektroninen julkaisu: | http://urn.fi/URN:NBN:fi:aalto-201608263118 |
| Sijainti: | P1 Ark Aalto 4400 | Arkisto |
| Avainsanat: | water poverty index spatial data mining Laos geographically weighted statistics local statistics monsoon seasons vesiköyhyysindeksi spatiaalinen datan louhinta maantieteellisesti painotettu regressio paikalliset tilastot monsuunisateet |
| Tiivistelmä (eng): | The United Nations recently revised and published new Sustainable Development Goals for the next 15 years. At least six of the 17 goals are directly linked to water, and several others are indirectly affected by water issues. Water is central to achieving the goals and water-related issues have been identified by the World Economic Forum as some of the biggest risks the world is facing in the future. Efficient measures to address the issues require integrated approaches, such as the Water Poverty Index (WPI). WPI is a holistic tool to assess water resources in an integrated way, combining water resource availability, social dimensions of access and capacity to manage water resource as well as the environmental requirements for utilization of water. This thesis examines the spatio-temporal distribution and the causes of water poverty in Lao PDR through WPI. Laos is located in Monsoon Asia with extreme seasonal differences in water availability. Due to this seasonality, WPI is developed in a manner that allows computing dry and wet season WPI separately and comparing them in a meaningful way. Exploratory (spatial) data analysis as well as spatial data mining methods are employed to investigate the distribution and causes of water poverty. The research is based on freely available data, and R code used in the analyses are openly published at http://markokallio.fi/waterpoverty/. Significant spatial and temporal differences are found. Water poverty is high in the rural areas and in the mountains, while the low-lying lands near the Mekong river exhibit relatively low water poverty. Three provinces; Xekong, Oudomxai and Phongsaly are very poor, while the area around Vientiane Capital show least water poverty. Major difference is found also between seasons with WPI increasing in the water-rich more than in the water-poor areas as the wet season starts. In addition, it was found that in some locations, water poverty is higher during the wet season than in the dry season. The main causes driving water poverty are found to be Use and Access related, and in relative terms, Capacity related (especially village road access). Resource availability is problematic mainly in the western and northwestern provinces during the dry season. |
| ED: | 2016-09-04 |
INSSI tietueen numero: 54329
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